1. Technical Field
The present teaching relates to methods, systems, and programming for identifying a target metric. Particularly, the present teaching relates to methods, systems, and programming for identifying a target metric for optimizing content personalization and recommendation.
2. Discussion of Technical Background
Personalized content recommendation systems are a subclass of information filtering systems that predict an “interest” that a user would have in online content (such as articles, news, music, books, or movies), using a model built based on the characteristics of users and the content related thereto and the user's online behaviors. Personalized content recommendation systems usually optimize towards a known short-term target, but may not be tuned/optimized towards long-term goals because the optimization needs to assign a “score” immediately at the time of the learning. Typically, machine learning ranking algorithms need a fine-granular learning target per article per user, in order to be able to recommend good articles for each different user. Therefore, the learning-target typically can only be computed within a short-time period. As a result, it is very difficult to train personalized content recommendation systems to optimize for long-term goals like user engagement.
Most known prior works targeted on short-term metrics, in particular, click-through rate (CTR), which, however, does not necessarily lead to the long-term engagement that is ultimately desired. CTR has been widely used because it has a direct, measurable impact on short-term revenue for example, through advertisement impressions. Although many believed that it does not necessarily lead to long-term engagement, there is no known way to provide a better short-term optimization target. Therefore, there is a need to provide an improved solution for identifying a target metric for optimizing personalized content recommendation systems to solve the above-mentioned problems.